Targeted advertising uses attributes or activities of a consumer to select an advertisement that may be of interest to that consumer. Selection of a targeted advertisement may be based on demographics (e.g., gender, age, ethnicity), psychographics (e.g., interests of a consumer), and past behaviors of the consumer (e.g., browsing history, purchase history, etc.). As an example, if a consumer (also referred to herein as a “user”) is browsing the Internet, then a back-end system may track searches, queries, and past purchases of the consumer, among other means, via “cookies” that are sent to and stored on the user device. The back-end system may then use an algorithm, such as a collaborative filtering algorithm, to select advertisements to display to the consumer while browsing. For instance, if the analyzed data predicts that a consumer of a certain category frequently purchases a product ‘y’ after having purchased product ‘x’, then the back-end system may determine that someone belonging to the same category and who has purchased product ‘x’ is likely to purchase product ‘y’. Consequently, the back-end system may select advertisements related to product ‘y’.
A consumer may have access to many different media channels other than just Internet. For example, a consumer may watch television, listen to the radio, have a mobile phone, etc. Unfortunately, each media channel provides advertisements to consumers independent of the other media channels, so there is no consistency of advertising among the different media channels.